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Introduction¶

This project explores customer investment behavior across various asset classes using a dataset of 12,000 transactions over multiple years. The goal is to uncover trends in asset preferences, track average returns over time, and segment users by investment size and frequency.

The dataset¶

This dataset contains the following features:

  • Customer ID: The customer's ID
  • Gender: Male or Female
  • Age: The customer's age
  • Region: The customer's state
  • Date: The date the investment was made
  • Investment Type: Stock, bond, crypto, etc.
  • Amount Invested: Customer's Capital
  • ROI: Customer's return on investment
  • Customer Join Date: Date customer joined
  • Risk Profile: Customer's risk profile
  • Last Investment Date: Date last investment was made by customer
CustomerID Gender Age Region Date InvestmentType Amount ROI CustomerJoinDate RiskProfile LastInvestmentDate
0 INV00088 Male 20 Port Harcourt 11/2/23 Mutual Fund 14174.79 668.30 1/25/22 Medium 9/27/24
1 INV00810 Male 39 Kano 10/9/24 Fixed Income 28842.26 641.62 11/18/23 Medium 12/26/24
2 INV00684 Female 41 Abuja 7/7/23 Mutual Fund 4411.04 216.18 5/14/22 Medium 5/9/24
3 INV01683 Other 23 Port Harcourt 6/24/23 Crypto 18967.37 4290.20 2/11/23 Medium 6/8/24
4 INV02582 Female 67 Abuja 10/14/24 Fixed Income 1440.70 45.99 10/4/21 Medium 10/14/24

Data Cleaning & Preparation¶

====== Data Information ======


<class 'pandas.core.frame.DataFrame'>
RangeIndex: 12000 entries, 0 to 11999
Data columns (total 11 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   CustomerID          12000 non-null  object 
 1   Gender              12000 non-null  object 
 2   Age                 12000 non-null  int64  
 3   Region              12000 non-null  object 
 4   Date                12000 non-null  object 
 5   InvestmentType      12000 non-null  object 
 6   Amount              12000 non-null  float64
 7   ROI                 12000 non-null  float64
 8   CustomerJoinDate    12000 non-null  object 
 9   RiskProfile         12000 non-null  object 
 10  LastInvestmentDate  12000 non-null  object 
dtypes: float64(2), int64(1), object(8)
memory usage: 1.0+ MB
None
====================


====== Descriptive Statistics ======
       CustomerID  Gender           Age Region    Date InvestmentType  \
count       12000   12000  12000.000000  12000   12000          12000   
unique       2935       3           NaN      5    1624              5   
top      INV02563  Female           NaN   Kano  2/8/24    Mutual Fund   
freq           12    5876           NaN   2547      25           2440   
mean          NaN     NaN     44.494167    NaN     NaN            NaN   
std           NaN     NaN     14.411880    NaN     NaN            NaN   
min           NaN     NaN     20.000000    NaN     NaN            NaN   
25%           NaN     NaN     32.000000    NaN     NaN            NaN   
50%           NaN     NaN     45.000000    NaN     NaN            NaN   
75%           NaN     NaN     57.000000    NaN     NaN            NaN   
max           NaN     NaN     69.000000    NaN     NaN            NaN   

               Amount           ROI CustomerJoinDate RiskProfile  \
count    12000.000000  12000.000000            12000       12000   
unique            NaN           NaN             1275           3   
top               NaN           NaN          12/6/20      Medium   
freq              NaN           NaN               37        5150   
mean     10239.886546    868.152366              NaN         NaN   
std      10084.656504   3506.280029              NaN         NaN   
min       1000.000000 -52934.200000              NaN         NaN   
25%       3035.020000     77.032500              NaN         NaN   
50%       7161.380000    328.600000              NaN         NaN   
75%      14144.792500    958.127500              NaN         NaN   
max     127979.250000  66492.460000              NaN         NaN   

       LastInvestmentDate  
count               12000  
unique                764  
top              12/28/24  
freq                   93  
mean                  NaN  
std                   NaN  
min                   NaN  
25%                   NaN  
50%                   NaN  
75%                   NaN  
max                   NaN  
====================


====== Unique Entries ======
CustomerID             2935
Gender                    3
Age                      50
Region                    5
Date                   1624
InvestmentType            5
Amount                10890
ROI                   11560
CustomerJoinDate       1275
RiskProfile               3
LastInvestmentDate      764
dtype: int64
====================

(None, None, None)

From the descriptive output above, we can see that there are 12000 non-null rows and 11 columns. We also have 3 numeric columns (Age, Amount, and ROI), while others are non-numeric. The data also showed that this company has 2935 unique customers, with ages ranging between 20-69 years. From the dataset, we also observe that investment amount ranges from ₦1,000 to ₦127,979 with a mean of ₦10,240; return on investment is highly variable (-₦52,934 to +₦66,492), with an average return of ₦868.

Looking at the dataset through the data cleaning lens, we notice that the dataset is largely clean, apart from the datatype issues with the date columns (Date,CustomerJoinDate, and LastInvestmentDate). This will be addressed immediately.

CustomerID Gender Age Region Date InvestmentType Amount ROI CustomerJoinDate RiskProfile LastInvestmentDate
0 INV00088 Male 20 Port Harcourt 2023-11-02 Mutual Fund 14174.79 668.30 2022-01-25 Medium 2024-09-27
1 INV00810 Male 39 Kano 2024-10-09 Fixed Income 28842.26 641.62 2023-11-18 Medium 2024-12-26
2 INV00684 Female 41 Abuja 2023-07-07 Mutual Fund 4411.04 216.18 2022-05-14 Medium 2024-05-09
3 INV01683 Other 23 Port Harcourt 2023-06-24 Crypto 18967.37 4290.20 2023-02-11 Medium 2024-06-08
4 INV02582 Female 67 Abuja 2024-10-14 Fixed Income 1440.70 45.99 2021-10-04 Medium 2024-10-14
... ... ... ... ... ... ... ... ... ... ... ...
11995 INV01870 Female 47 Kano 2023-07-09 Mutual Fund 1193.03 89.93 2022-06-13 Medium 2024-12-07
11996 INV01632 Male 38 Kano 2022-03-05 Fixed Income 21248.03 788.98 2021-06-06 Low 2023-09-27
11997 INV01144 Male 31 Lagos 2021-01-06 Crypto 6204.79 30.79 2020-09-01 Medium 2024-11-20
11998 INV00882 Female 44 Kano 2024-01-19 Fixed Income 17048.94 254.48 2022-02-12 Low 2024-01-19
11999 INV02898 Female 54 Enugu 2021-04-28 Real Estate 13841.56 990.36 2021-02-22 High 2024-12-22

12000 rows × 11 columns

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 12000 entries, 0 to 11999
Data columns (total 11 columns):
 #   Column              Non-Null Count  Dtype         
---  ------              --------------  -----         
 0   CustomerID          12000 non-null  object        
 1   Gender              12000 non-null  object        
 2   Age                 12000 non-null  int64         
 3   Region              12000 non-null  object        
 4   Date                12000 non-null  datetime64[ns]
 5   InvestmentType      12000 non-null  object        
 6   Amount              12000 non-null  float64       
 7   ROI                 12000 non-null  float64       
 8   CustomerJoinDate    12000 non-null  datetime64[ns]
 9   RiskProfile         12000 non-null  object        
 10  LastInvestmentDate  12000 non-null  datetime64[ns]
dtypes: datetime64[ns](3), float64(2), int64(1), object(5)
memory usage: 1.0+ MB

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 12000 entries, 0 to 11999
Data columns (total 15 columns):
 #   Column              Non-Null Count  Dtype         
---  ------              --------------  -----         
 0   CustomerID          12000 non-null  object        
 1   Gender              12000 non-null  object        
 2   Age                 12000 non-null  int64         
 3   Region              12000 non-null  object        
 4   Date                12000 non-null  datetime64[ns]
 5   InvestmentType      12000 non-null  object        
 6   Amount              12000 non-null  float64       
 7   ROI                 12000 non-null  float64       
 8   CustomerJoinDate    12000 non-null  datetime64[ns]
 9   RiskProfile         12000 non-null  object        
 10  LastInvestmentDate  12000 non-null  datetime64[ns]
 11  Year                12000 non-null  int32         
 12  Month               12000 non-null  int32         
 13  CustomerTenureDays  12000 non-null  int64         
 14  IsHighROI           12000 non-null  int64         
dtypes: datetime64[ns](3), float64(2), int32(2), int64(3), object(5)
memory usage: 1.3+ MB

Exploratory Analysis¶

From early 2020 to late 2024, monthly investments showed a consistent upward trend, indicating growing investor activity or confidence, with a notably steeper increase in the rate of investment growth occurring around early 2021, potentially due to market recovery, policy changes, or successful financial products. This growth culminated in peak monthly investment values exceeding 4.5 million in early to mid-2024, suggesting a period of heightened investor interest or market performance.

Next, we turn our attention to asset classes to understand how investors interacted with different investment instruments during the period.

  InvestmentType    Amount
2    Mutual Fund  10480.85
3    Real Estate  10263.28
4         Stocks  10242.47
1   Fixed Income  10113.90
0         Crypto  10097.39

The average investment amounts across all asset classes are relatively uniform, ranging narrowly from approximately ₦10,100 to ₦10,500, suggesting a balanced investment strategy among investors. Notably, Mutual Funds exhibited the highest average investment at ₦10,480.85, indicating stronger investor confidence or popularity, while Crypto shows the lowest average investment at around ₦10,097.39, potentially reflecting perceived volatility or risk aversion. The minimal gaps between these averages imply a tendency towards even diversification rather than a heavy preference for a single asset class.

The chart above visualizes the interplay between investment returns, investor age, and risk tolerance, categorized as Low, Medium, and High. Each data point on the scatter plot represents a single investment, positioned horizontally by the investor's age and vertically by the achieved Return on Investment (ROI). The color of each point further distinguishes the investor's risk profile: blue signifies Medium risk, green indicates Low risk, and red denotes High risk.

Analysis of the chart reveals several key patterns. Investors across all age groups and risk profiles have experienced a diverse range of ROI outcomes, spanning from significant gains to substantial losses. Notably, there is no discernible trend suggesting a direct correlation between investor age and investment returns across the different risk categories. Furthermore, a higher concentration of investments appears around the lower positive and negative ROI values, implying that extreme investment outcomes might be less common. High-risk investments exhibit a wider dispersion of ROI, including both higher potential gains and losses, while low-risk investments tend to cluster within a narrower range of returns, typically closer to the zero mark.

The correlations between Amount and Age (-0.0), Amount and CustomerTenureDays (-0.01), ROI and Age (0.01), and ROI and CustomerTenureDays (0.01) are all very weak or negligible, indicating practically no linear relationship between these pairs of variables. A weak positive correlation (0.23) exists between Amount and ROI, suggesting a slight tendency for larger investments to be associated with somewhat higher returns, although this relationship is not strong.

          Region    Amount
0          Abuja  10542.71
4  Port Harcourt  10393.56
1          Enugu  10206.16
2           Kano  10102.78
3          Lagos   9983.60

The bar chart above displays the average investment amount across five different regions: Abuja, Port Harcourt, Enugu, Kano, and Lagos. The height of each bar corresponds to the average investment amount for that specific region, with Abuja exhibiting the highest average investment at approximately ₦10,542.71, followed closely by Port Harcourt at around ₦10,393.56 and Enugu at roughly ₦10,206.16. Kano shows a slightly lower average investment of about ₦10,102.78, while Lagos has the lowest average investment among the displayed regions at approximately ₦9,983.60. Overall, the average investment amounts are relatively similar across these regions, with Abuja showing a marginal lead and Lagos having the lowest average investment.

Insights & Recommendations

  • Capitalize on the Growth Trend: Leverage the observed upward trend in monthly investments from 2020 to late 2024 by understanding and potentially reinforcing the factors that contributed to this growth, particularly the significant surge after early 2021. This might involve further investigating the market recovery, policy changes, or the success of specific financial products that drove this increase.
  • Explore Mutual Fund Popularity: Further analyze why Mutual Funds have the highest average investment. Understanding the specific characteristics or investor perceptions driving this preference can inform strategies for other asset classes.
  • Address Crypto Investment Aversion: Investigate the reasons for the lower average investment in Crypto despite its hype. Understanding if this stems from perceived volatility, risk aversion, or lack of understanding can guide efforts to educate investors or potentially adjust offerings.
  • Monitor Diversification Trends: Continue to observe the relatively uniform average investment across asset classes, which suggests a balanced diversification strategy. Understanding the drivers behind this could inform the development of products or advice that aligns with this preference.
  • Further Investigate ROI by Risk Profile and Age: While no strong correlation was immediately apparent, the wider ROI range for high-risk investments and the narrower range for low-risk investments align with expectations. Further analysis could explore specific investment strategies within each risk profile and their performance across different age groups.
  • Regional Investment Strategies: Acknowledge the slightly higher average investment in Abuja and the slightly lower average in Lagos. Further investigation into the economic factors or investor demographics in these regions could inform targeted strategies or product offerings.

¶

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